[Coin-announce] Balans solver joins the COIN-OR Foundation

[Serdar Kadioglu]

Dear COIN-OR Community,

We are pleased to announce that Balans solver
<https://github.com/coin-or/balans> has now officially joined the COIN-OR
Foundation.

*B*andits-based *A*daptive *LA*rge *N*eighborhood *S*earch (Balans) is an
online meta-solver for solving Mixed-Integer Programming (MIP) problems. It
utilizes adaptive large neighborhood search (ALNS), guided by multi-armed
bandits (MAB), operating on top of a MIP solver. More details are available
in our IJCAI'25 <https://www.ijcai.org/proceedings/2025/286> paper and
slides
<https://nbviewer.org/github/skadio/skadio.github.io/blob/master/files/2025_IJCAI_Balans_Kadioglu.pdf>.


The project is open-source under the Apache 2.0 License and now lives at:
https://github.com/coin-or/balans

Install Balans directly via pip:  pip install balans  and here is a Quick
Start Example
<https://github.com/coin-or/balans?tab=readme-ov-file#quick-start> with key
features summarized below.

We look forward to your feedback and contributions!

Best regards,
Serdar Kadioglu & Bistra Dilkina
On behalf all the Balans development team

===

Balans Solver Key features:

   - *Solver-Agnostic:* Supports SCIP & Gurobi out-of-the-box, and any MIP
   solver implementing a fairly standard abstract base MIP class
   <https://github.com/coin-or/balans/blob/main/balans/base_mip.py>.
   - *Flexible:* Running a single destroy/repair neighborhood recovers
   LNS(MIP). The API allows you to choose which neighborhoods to include,
   reward mechanism, and learning strategy.
   - *Extensible: *Built on top of the open-source MabWiser library
   <https://github.com/fidelity/mabwiser> (from Fidelity Investments) and
   the open-source ALNS library <https://github.com/N-Wouda/ALNS> (from
   RoutingLab), it remains highly modular and configurable.
   - *Parallelizable:* ParBalans allows controlling parallel Balans
   configurations and parallel MIP solving.
   - *Integration Technology: *Broadly, Balans combines adaptive search,
   meta-heuristics, multi-armed bandits, and mixed integer programming in a
   unified framework.
   - *Contributions:* New MIP solver integrations, new destroy/repair
   neighborhoods, and new learning strategies among others, are most welcome!
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